A Multi-Domain Intelligent Framework for Biomedical Prediction and Healthcare Risk Analytics Using TJO and Tree Growth-Driven Deep Neural Networks

Abstract

The rapid digitization of healthcare and biomedical informatics has led to the generation of massive multi-modal datasets derived from electronic health records (EHRs), medical imaging, genomic data, and wearable sensors. These data sources enable predictive analytics for early disease detection and patient risk assessment but simultaneously present challenges of data heterogeneity, high dimensionality, and parameter optimization. Traditional predictive models often struggle to balance diagnostic accuracy, interpretability, and computational efficiency. This paper introduces a multi-domain intelligent framework that integrates Tunicate Swarm Optimization (TJO) and Tree Growth Algorithm (TGA) with Deep Neural Networks (DNNs) for robust biomedical prediction and healthcare analytics. TJO is used for feature selection, reducing redundant clinical attributes, while TGA adaptively tunes DNN hyperparameters to improve convergence and precision. The proposed system is validated across diverse healthcare domains, including disease risk classification, patient outcome forecasting, and biomedical signal prediction. Experimental results demonstrate superior performance in prediction accuracy, computational efficiency, and model generalization compared to traditional optimization-driven neural networks. This framework establishes a scalable foundation for unified, intelligent, and interpretable predictive modeling in healthcare informatics.